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基于5G车用无线通信技术网络的工业园区多层协同框架
Yanjun Shi, Qiaomei Han, Weiming Shen *, Xianbin Wang
工程（英文） ›› 2021, Vol. 7 ›› Issue (6) : 818-831.
PDF(3354 KB)
PDF(3354 KB)
基于5G车用无线通信技术网络的工业园区多层协同框架
A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks
第五代（5G）无线通信网络有望在垂直产业转型中发挥重要的作用。在众多激动人心的5G应用中，通过车用无线通信技术（V2X）通信可更高效地执行工业园区内的物流任务。本文提出了一种基于V2X的工业园区物流管理多层协同框架。该框架包括三层：感知与执行层、物流层以及配置层。除以上三层之间的协同外，本研究还讨论了设备、边缘服务器以及云服务之间的协同。针对工业园区内的高效物流，可通过四项功能来实现任务协同，这四项功能分别是：环境感知与地图构建、任务分配、路径规划，以及车辆运动。为动态协调这些功能，将采用5G切片和V2X通信技术支持的设备边云协同。随后，利用目标级联分析法对工业园区协同方案进行配置和评估。最后，通过一工业园区物流分析案例，验证了所提出协同框架的可行性。
The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device–edge–cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.
5G / 车用无线通信技术 / 工业园区 / 物流 / 设备边云协同 / 目标级联分析
5G / Vehicle-to-everything / Industrial park / Logistics / Device–edge–cloud collaboration / Analytical target cascading
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